1. Field of the Present Invention
The present invention relates generally to infrastructure modeling and simulation, and, in particular, to the modeling and simulation of interdependencies between or among a plurality of infrastructures that may be of different types.
2. Background
As is well known, civilization as we know it today is built or reliant upon a wide variety of infrastructures. The infrastructures of which we are most widely aware are civilization's physical infrastructures, such as electrical power grids, gas distribution networks, transportation networks (including roadways, rail networks, waterways, aviation, and the like), and information and telecommunication systems. Other physical infrastructures may include agricultural and food-related infrastructures, water distribution and recovery networks and infrastructures, public health and emergency services infrastructures, government institutions and infrastructures, defense industrial base infrastructures, banking and finance (including economic) networks and infrastructures, chemical industry and hazardous materials systems and infrastructures, national monuments and icons, and the like. Some of these are often referred to as “critical” infrastructures because they are viewed as being so vital to a particular geopolitical unit that the incapacity or destruction of such systems and assets would have a debilitating impact on control or defense, economic security, public health or safety, or any combination of those matters.
FIG. 1 is a graphical illustration of some of the most critical physical infrastructures known today. As evident therefrom, these infrastructures relate to each other in a variety of ways, some of which are shown therein. For example, petroleum provides fuel and lubricants for vehicles used in the transportation industry, which in turn is used to ship materials in the natural gas industry. Natural gas is used in the electric power industry, whose electricity in turn supports the telecom industry. Telecom provides SCADA and communications for water plants, and water is used in production, cooling and emissions reduction in the petroleum industry. A wide variety of additional interdependencies, some of which are illustrated in FIG. 1, are likewise known.
However, today's civilization depends not just on physical systems or assets such as the infrastructures shown in FIG. 1, but on at least two other broad categories of infrastructures as well. For example, another underlying foundation of today's civilization is the way people or groups of people behave in response to various stimuli, phenomena sometimes referred to as “population behavior.” Population behavior models are useful for studying and simulating emergent patterns of situational human interactions and geospatial movements, including such phenomena as traffic patterns, crowd movement, disease transmission, area ingress and egress, and the like. As with physical infrastructure models, such models may incorporate geospatial relationships, but are focused on behavioral patterns and phenomena rather than on the physical (non-human) relationships between different elements of one or more physical phenomena.
Still another basic framework of today's civilization includes the various social networks that exist and connect every segment of civilization, which may collectively be referred to as “intangible relationships and influences” or “social networks.” Models of this type are useful for studying and simulating both formal and informal organizations of people. Formal organizations may include government organizations, political organizations, business organizations, religious organizations, and other non-state organizations. Information organizations may include clans, families, people connected by common belief systems, friendships and acquaintances, and the like.
As used herein, then, the term “infrastructure” may refer to any fundamental framework or underlying foundation pertaining to any physical, relational or behavioral aspect of a civilization, where “behavioral infrastructures” are defined as infrastructures involving the way people or groups of people behave in response to various stimuli, “relational infrastructures” are defined as infrastructures created by human interactions in any of the various social networks that exist in and connect various segments of the population of a civilization, and “physical infrastructures” are defined as infrastructures involving physical assets or systems. Furthermore, although sometimes used interchangeably with the term “infrastructure,” the term “network,” as used herein, generally refers collectively to the various connections between or among elements of a given infrastructure, rather than the infrastructure as a whole. In physical infrastructures, such networks often have a physical relevance, but in many infrastructures, and particularly behavioral and relational infrastructures, networks may have no direct physical relevance.
In order to understand how a given infrastructure works, researchers, planners, government bodies and other parties with varying interests in the infrastructure typically devote considerable effort to studying various aspects of the infrastructure. More particularly, the operation of each of a great many infrastructures has been simulated, using tools of varying degrees of sophistication, to make it possible to study the effect of various stimuli (both internal and external) on the operation of the respective infrastructure. Given the wide variation between different types of infrastructures, these efforts have collectively involved large numbers of people of extremely disparate technological backgrounds or interests using a wide variety of techniques. However, as computer technology has become increasingly sophisticated, complex software models have been developed to make it possible to analyze huge amounts of data representing the various elements of each infrastructure and their interrelationships. Unfortunately, such software models have generally been created to focus only on a single infrastructure at a time. Those few software models that are capable of simulating more than one infrastructure at a time have been limited in capability to the simulation of infrastructures of only a single type. Thus, a software-based simulation approach is needed that is capable of handling, on a generic basis, a plurality of models representing infrastructures of different fundamental types or categories.
Furthermore, even those software-based simulation approaches that are capable, at least to some degree, of modeling the interrelationships between or among multiple infrastructures have had success only in certain areas. More particularly, these approaches have been deficient in modeling the interrelationships between or among critical infrastructures, as that term is defined hereinabove. Perhaps even more than some other types of infrastructures, critical infrastructures typically involve multi-dimensional, highly complex collections of technologies, processes, and people, and as such, are vulnerable to potentially catastrophic failures (intentional or unintentional) on many levels. Because of their importance and fundamental relationship with the day-to-day operation of a civilization and its various geopolitical units, it is often particularly useful to study these critical infrastructures and their interrelationships with each other. Unfortunately, given the breadth and depth of these infrastructures, one can readily observe characteristics that make the problem of protecting a nation's critical infrastructures, in general, intractable. Key among these characteristics is the inherent complexity of these infrastructures, each defining a unique field of research with numerous open problems regarding organization, operation, and evolution. Magnifying these challenges and the dangers that arise are numerous inherent interdependencies that exist among critical infrastructures, interdependencies that are commonly very strong, time-sensitive, and essential. The result is a brittle “system of systems” that could lead to catastrophic occurrences as a failure (intentional or unintentional) cascades and escalates across infrastructures.
Despite its inherent difficulties, the problem of understanding the behavior of critical infrastructures and their interdependence has been an integral part of well-established disciplines, such as urban and regional planning, civil and environmental engineering, operations research, landscape architecture, and emergency management. This has been discussed, for example, by Kaiser et al. in Urban Land Use Planning, 4th Edn., published by University of Illinois Press (Urbana, Ill. 1995), which is incorporated herein by reference. More recently, as a key area of inquiry, it is receiving increasing attention from the emerging field of geographic information science and technology (“GI S&T”), including in Sinton, D. F.: “Reflections on 25 years of GIS,” GIS World Vol. 5. No. 2 (1992) (“Sinton”) and in 1-8 UCGIS: University Consortium for Geographic Information Science (2003), each of which is incorporated herein by reference. Researchers in the GI S&T community have primarily used three different approaches in studying the behavior and spatial interdependence of critical infrastructures. In a first approach, sometimes referred to as spatial data analysis (“SDA”), researchers have examined the interdependence of critical infrastructures with tools from spatial statistics and econometrics. This has been described, for example, in Cressie, N.: “Statistics for Spatial Data,” published by John Wiley (Chichester, 1991) and Haining, R.: “Spatial data analysis in the social and environmental sciences,” published by Cambridge University Press (Cambridge, UK, 1990), each of which is incorporated herein by reference. In a second approach, geographic correlations among critical infrastructure components are depicted using traditional map overlay methods for spatial data aggregation in GIS environments. This has been described, for example, in Burrough, P. A.: “Methods of spatial analysis in GIS,” published in International Journal of Geographical Information Systems, Vol. 4. No. 3 (1990), 221-223; Goodchild, M. F. and Kemp, K. K.: NCGIA core curriculum (University of California at Santa Barbara, Calif., 1990); and Greene, R. W.: “Confronting Catastrophe: a GIS Handbook” (ESRI Press, Redlands Calif., 2002), each of which is incorporated herein by reference. In a third approach, rule-based inference engines, usually fueled by human expert's knowledge, are used in the delineation and manipulation of interdependence. This has been described, for example, by Gronlund, A. G. et al.: “GIS, expert systems technologies improve forest fire management techniques,” published in GIS World, Vol. 7. No. 2 (1994), pages 32-36, and Xiang, W.-N.: “Knowledge-based decision support by CRITIC,” published in Environment and Planning B: Planning and Design, Vol. 24. No. 1 (1997), pages 69-79, each of which is incorporated herein by reference. Unfortunately, these approaches, while informative, do not in isolation adequately address the problem regarding the impact of critical infrastructure interdependencies.
Consequently, many respected GI S&T researchers have advocated a multi-dimensional approach to the study of behavior and spatial interdependence of critical infrastructures. This approach is described, for example, in Getis, A.: “Spatial dependence and heterogeneity and proximal databases,” in Spatial Analysis and GIS (Eds.: FotheringHam, S. and Rogerson, P.) (Taylor & Francis, London, 1994), pages 105-120 (“Getis”), which is incorporated herein by reference, and Sinton. Instead of “divide-and-conquer,” they suggested a strategy that combines strengths of the three different approaches described above and investigates the matter of interdependence from all three vantage points. However, despite some genuine efforts, such as those described in Anselin, L. and Getis, A.: “Spatial statistical analysis and geographic information systems,” published in Annals of Regional Science, Vol. 26 (1992), pages 19-33; Flowerdew, Green: “A real interpolation and types of data,” in Spatial Analysis and GIS (Eds.: FotheringHam, S. and Rogerson, P.) (Taylor & Francis, London, 1994), pages 121-145; Getis; and Openshaw: “Two exploratory space-time-attribute pattern analysers relevant to GIS,” in Spatial Analysis and GIS (Eds.: FotheringHam, S. and Rogerson, P.) (Taylor & Francis, London, 1994), pages 82-104, each of which is incorporated herein by reference, progress along this route has yet to meet the advocates' expectations. The status quo is exemplified by some more recent publications in which little if any multidimensional GI S&T-based results were reported, including Mitchell, A.: “The ESRI Guide to GIS Analysis” (ESRI Press, Redlands, Calif., 1999) and Zeiler, M.: “Modeling Our World: the ESRI Guide to Geodatabase Design” (ESRI Press, Redlands, Calif., 1999), each of which is incorporated herein by reference.
There are also several feature-rich GI S&T visualization tools (e.g. ESRI ArcGIS) for overlaying critical infrastructure components onto maps and satellite imagery. These tools often support single infrastructure modeling and simulation—e.g., analyses that can determine the impact of a failed power line on electrical power distribution. However, to date, such models are relatively static and do not support interactions across infrastructure layers.
A more sophisticated approach may be offered by modeling and simulating critical infrastructure interdependencies. Although generally not yet attempted by any of the disciplines described above, this approach has received some attention within the modeling and simulation community. For example, Rinaldi, S. M. et al.: “Identifying, understanding, and analyzing critical infrastructure interdependencies,” published in IEEE Control Systems Magazine (December 2001), pages 11-25 (“Rinaldi et al.”), which is incorporated herein by reference, suggest a taxonomy for identifying, understanding, and analyzing critical infrastructure interdependencies based upon interdependency type. Rinaldi et al. also contend that intelligent agent-based techniques may be appropriate for modeling such interdependencies. The application of intelligent agent-based techniques to modeling and simulation problems is well-understood for many problem domains, as discussed, for example, in Weiss, G. ed.: Multiagent Systems (MIT Press, 1999) (“Weiss”), which is incorporated herein by reference. Unfortunately, there are no known applications of this approach to critical infrastructure integration.
The problem of understanding the behavior of critical infrastructures and their interdependence remains a difficult, open problem. The limitations of single-dimensional approaches are by no means trivial. Multi-dimensional GI S&T approaches, while theoretically promising, have produced few results. Furthermore, while modeling and simulation can lead to a better understanding of the behavior of critical infrastructures, no prior art solution is comparable to that of the present invention.
In view of all of the foregoing, a need exists for a software-based simulation approach that is capable of handling, on a generic basis, a plurality of models representing infrastructures of different fundamental types. Moreover, a particular need exists for a software-based simulation approach that is capable of handling a plurality of models representing different critical infrastructures. Finally, a need exists for a “software agent”-based approach to modeling and/or simulating one or more critical infrastructures with intra-dependencies and/or interdependencies.